Bastola, S., Misra, V., & Li, H. (2013). Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons.
Earth Interact., 17(25), 1–22.
Cintra, R., Campos Velho, H., & Cocke, S. (2016). Multilayer Perceptron on data assimilation system applied to FSU global model..
Davidson, F., Alvera-Azcárate, A., Barth, A., Brassington, G. B., Chassignet, E. P., Clementi, E., et al. (2019). Synergies in Operational Oceanography: The Intrinsic Need for Sustained Ocean Observations.
Front. Mar. Sci., 6.
Abstract: Operational oceanography can be described as the provision of routine oceanographic information needed for decision-making purposes. It is dependent upon sustained research and development through the end-to-end framework of an operational service, from observation collection to delivery mechanisms. The core components of operational oceanographic systems are a multi-platform observation network, a data management system, a data assimilative prediction system, and a dissemination/accessibility system. These are interdependent, necessitating communication and exchange between them, and together provide the mechanism through which a clear picture of ocean conditions, in the past, present, and future, can be seen. Ocean observations play a critical role in all aspects of operational oceanography, not only for assimilation but as part of the research cycle, and for verification and validation of products. Data assimilative prediction systems are advancing at a fast pace, in tandem with improved science and the growth in computing power. To make best use of the system capability these advances would be matched by equivalent advances in operational observation coverage. This synergy between the prediction and observation systems underpins the quality of products available to stakeholders, and justifies the need for sustained ocean observations. In this white paper, the components of an operational oceanographic system are described, highlighting the critical role of ocean observations, and how the operational systems will evolve over the next decade to improve the characterization of ocean conditions, including at finer spatial and temporal scales.
Engelman, M. B. (2008).
A Validation of the FSU/COAPS Climate Model. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: This study examines the predictability of the Florida State University/Center for Oceanic and Atmospheric Prediction Studies (FSU/COAPS) climate model, and is motivated by the model's potential use in crop modeling. The study also compares real-time ensemble runs (created using persisted SST anomalies) to hindcast ensemble runs (created using weekly updated SST) to asses the effect of SST anomalies on forecast error. Wintertime (DJF, 2 month lead time) surface temperature and precipitation forecasts over the southeastern United States (Georgia, Alabama, and Florida) are evaluated because of the documented links between tropical Pacific SST anomalies and climate in the southeastern United States during the winter season. The global spectral model (GSM) runs at a T63 resolution and then is dynamically downscaled to a 20 x 20 km grid over the southeastern United States using the FSU regional spectral model (RSM). Seasonal, monthly, and daily events from the October 2004 and 2005 model runs are assessed. Seasonal (DJF) plots of real-time forecasts indicate the model is capable of predicting wintertime maximum and minimum temperatures over the southeastern United States. The October 2004 and 2005 real-time model runs both produce temperature forecasts with anomaly errors below 3°C, correlations close to one, and standard deviations similar to observations. Real-time precipitation forecasts are inconsistent. Error in the percent of normal precipitation vary from greater than 100% in the 2004/2005 forecasts to less than 35% error in the 2005/2006 forecasts. Comparing hindcast runs to real-time runs reveals some skill is lost in precipitation forecasts when using a method of SST anomaly persistence if the SST anomalies in the equatorial Pacific change early in the forecast period, as they did for the October 2004 model runs. Further analysis involving monthly and daily model data as well as Brier scores (BS), relative operating characteristics (ROC), and equitable threat scores (ETS), are also examined to confirm these results.
Fu, C. B., Qian, C., & Wu, Z. H. (2011). Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach.
Sci. China Earth Sci., 54(9), 1400–1406.
Goff, J. A., & Arbic, B. K. (2010). Global prediction of abyssal hill roughness statistics for use in ocean models from digital maps of paleo-spreading rate, paleo-ridge orientation, and sediment thickness.
Ocean Modelling, 32(1-2), 36–43.
Hong, S. - Y., Park, H., Cheong, H. - B., Kim, J. - E. E., Koo, M. - S., Jang, J., et al. (2013). The Global/Regional Integrated Model system (GRIMs).
Asia-Pacific J Atmos Sci, 49(2), 219–243.
Jagtap, S. S., Jones, J. W., Hildebrand, P., Letson, D., O'Brien, J. J., Podestá, G., et al. (2002). Responding to stakeholder's demands for climate information: from research to applications in Florida.
Agricultural Systems, 74(3), 415–430.
Kelly, D. L., Letson, D., Nelson, F., Nolan, D. S., & Solís, D. (2012). Evolution of subjective hurricane risk perceptions: A Bayesian approach.
Journal of Economic Behavior & Organization, 81(2), 644–663.
Kirtman, B. P., Misra, V., Anandhi, A., Palko, D., & Infanti, J. (2017). Future Climate Change Scenarios for Florida. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.),
Florida's climate: Changes, variations, & impacts (pp. 533–555). Gainesville, FL: Florida Climate Institute.